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AC和PA的SMC进一步分析

source("tianfengRwrappers.R")
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If you use clusterProfiler in published research, please cite:
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library(org.Hs.eg.db)
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library(msigdbr)
library(GSVA)
library(fgsea)
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library(UCell)

分离AC和PA的SMC亚群

```r
select.cells <- CellSelector(plot = DimPlot(ds2, reduction = \umap\)) #去除边角的离群细胞
ds2 <- subset(ds2, cell = select.cells)
# saveRDS(ds2,\ds2.rds\)
umapplot(ds2,split.by = \conditions\)
ds2 <- ds2 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.15)
umapplot(ds2, group.by = \seurat_clusters\,split.by = \conditions\)
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = \AC\)
ds2_PA <- subset(ds2, idents = \PA\)
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)
umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

## find markers

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZHMyX21hcmtlcnMgPC0gRmluZEFsbE1hcmtlcnMoZHMyLCBsb2dmYy50aHJlc2hvbGQgPSAwLjUsIG1pbi5kaWZmLnBjdCA9IDAuMiwgb25seS5wb3MgPSBGKVxuYGBgIn0= -->

```r
ds2_markers <- FindAllMarkers(ds2, logfc.threshold = 0.5, min.diff.pct = 0.2, only.pos = F)
Calculating cluster SMC1
Calculating cluster Fibromyocyte
Calculating cluster Pericyte
Calculating cluster Fibroblast
Calculating cluster SMC2
ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]

GO and KEGG

GSVA

exprmat <- get_data_table(ds2, highvar = T,type = "data")
clusterinfo <- ds2@meta.data[,c("orig.ident","Classification1")]
mbd <- msigdbr(species = "Homo sapiens", category = "C7") # C7 免疫
msigdbr_list <- split(x = mbd$gene_symbol, f = mbd$gs_name)

immo_res <- gsva(exprmat, msigdbr_list, kcdf="Gaussian",method = "gsva", parallel.sz = 6) #gsva 在server上运行
pheatmap(immo_res, show_rownames=1, show_colnames=0, 
         annotation_col=clusterinfo,fontsize_row=5, wiidth=8, height=6)#绘制热图
es <- data.frame(t(immo_res),stringsAsFactors=F)  #添加到单细胞矩阵中,可视化相关通路的在umap上聚集情况,可理解为一个通路即一个基因
dataset1 <- AddclusterinfoData(pbmc, es)
FeaturePlot(dataset1, features = "KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS", reduction = 'umap')

#GSEA

addmodulescore

geneset <- read.table("fibromyo")
dataset1 <- AddModuleScore(dataset1,features = geneset, name = 'fibromyo_score')
f("fibromyo_score1", dataset1, min.cutoff = 0)
dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')
f("V1fibromyo_score", dataset1)

SMC2

ds2

umapplot(ds2)
ds2_markers <- FindMarkers(ds2,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)
ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]

ds1

umapplot(ds1)

ds1_markers <- FindMarkers(ds1,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)

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ds1_markers_pos <- ds1_markers[ds1_markers$avg_logFC>0, ]
ds1_markers_neg <- ds1_markers[ds1_markers$avg_logFC<0, ]
gene_list <- rownames(ds1_markers_pos)
up_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC,也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(up_enrich.go, showCategory = 10)

cnetplot(up_enrich.go, showCategory = 10)

emapplot(up_enrich.go, showCategory = 10)


##down-regulated genes
gene_list <- rownames(ds1_markers_neg)
down_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC,也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(down_enrich.go, showCategory = 10)

cnetplot(down_enrich.go, showCategory = 10)

emapplot(down_enrich.go, showCategory = 10)

BMP通路相关

雷达图

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# AC和PA的SMC进一步分析
```{r}
source("tianfengRwrappers.R")
library(org.Hs.eg.db)
library(msigdbr)
library(GSVA)
library(fgsea)
library(UCell)
ds2 <- readRDS("ds2.rds")
ds0 <- readRDS("ds0.rds")
ds1 <- readRDS("ds1.rds")
```

## 分离AC和PA的SMC亚群
```{r}
select.cells <- CellSelector(plot = DimPlot(ds2, reduction = "umap")) #去除边角的离群细胞
ds2 <- subset(ds2, cell = select.cells)
# saveRDS(ds2,"ds2.rds")
umapplot(ds2,split.by = "conditions")
ds2 <- ds2 %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.15)
umapplot(ds2, group.by = "seurat_clusters",split.by = "conditions")
Idents(ds2) <- ds2$conditions
ds2_AC <- subset(ds2, idents = "AC")
ds2_PA <- subset(ds2, idents = "PA")
ds2_AC <- ds2_AC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)
ds2_PA <- ds2_PA %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.1)

umapplot(ds2_AC) + scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)
umapplot(ds2_PA)+ scale_y_continuous(limits = c(-5,15),breaks = NULL) +
        scale_x_continuous(limits = c(-5,15),breaks = NULL)
```
## find markers
```{r}
ds2_markers <- FindAllMarkers(ds2, logfc.threshold = 0.5, min.diff.pct = 0.2, only.pos = F)
ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]
```

## GO and KEGG
```{r,fig.width=8,fig.height=6}
##up-regulated genes
gene_list <- ds2_markers_pos[ds2_markers_pos$cluster == "SMC2",]$gene

up_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(up_enrich.go, showCategory = 10) + theme_classic()
enrichplot::cnetplot(up_enrich.go, showCategory = 5, colorEdge = T)
emapplot(up_enrich.go, showCategory = 10)

##down-regulated genes
gene_list <- ds2_markers_neg[ds2_markers_neg$cluster == 2,]$gene
down_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(down_enrich.go, showCategory = 10)
cnetplot(down_enrich.go, showCategory = 10)
emapplot(down_enrich.go, showCategory = 10)

```


## GSVA
```{r}
exprmat <- get_data_table(ds2, highvar = T,type = "data")
clusterinfo <- ds2@meta.data[,c("orig.ident","Classification1")]
mbd <- msigdbr(species = "Homo sapiens", category = "C7") # C7 免疫
msigdbr_list <- split(x = mbd$gene_symbol, f = mbd$gs_name)

immo_res <- gsva(exprmat, msigdbr_list, kcdf="Gaussian",method = "gsva", parallel.sz = 6) #gsva 在server上运行
pheatmap(immo_res, show_rownames=1, show_colnames=0, 
         annotation_col=clusterinfo,fontsize_row=5, wiidth=8, height=6)#绘制热图
```


```{r}
es <- data.frame(t(immo_res),stringsAsFactors=F)  #添加到单细胞矩阵中，可视化相关通路的在umap上聚集情况，可理解为一个通路即一个基因
dataset1 <- AddclusterinfoData(pbmc, es)
FeaturePlot(dataset1, features = "KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS", reduction = 'umap')


```

#GSEA
```{r}
library(clusterProfiler)
library(enrichplot)
markers <- FindMarkers(ds2, ident.1 = "SMC2",min.pct = 0.1, logfc.threshold = 0.1,thresh.use = 0.9)
DEGs <- markers$avg_logFC
names(DEGs) = rownames(markers)
DEGs <- sort(DEGs,decreasing = T)
head(DEGs)

# GO_db <- msigdbr(species = "Homo sapiens",category = "C5") %>%
  # dplyr::select(gs_exact_source,gene_symbol) #C5 GO  C7 免疫

mdb_c2 <- msigdbr(species = "Homo sapiens", category = "C2")
mdb_kegg <-  mdb_c2 [grep("^KEGG", mdb_c2 $gs_name),]
GO_db<-mdb_kegg %>% dplyr::select(gs_name, gene_symbol)

GSEA_res <- clusterProfiler::GSEA(DEGs, TERM2GENE = GO_db,pvalueCutoff = 0.1)

dotplot(GSEA_res,split=".sign")+facet_grid(~.sign) + theme_classic()

enrichplot::gseaplot2(GSEA_res, geneSetID = 1, title = GSEA_res$Description[1])

# for(i in seq_along(GSEA_res@result$ID)){
#   enrichplot::gseaplot2(GSEA_res, geneSetID = i, title = GSEA_res@result$ID[i])
# }
ridgeplot(GSEA_res) 
gseaplot2(GSEA_res,1:5)
```


#  addmodulescore
```{r}
geneset <- read.table("fibromyo")
dataset1 <- AddModuleScore(dataset1,features = geneset, name = 'fibromyo_score')
f("fibromyo_score1", dataset1, min.cutoff = 0)
dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')
f("V1fibromyo_score", dataset1)
```

# SMC2
ds2
```{r fig.width=8,fig.height=4}
umapplot(ds2)
ds2_markers <- FindMarkers(ds2,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)
ds2_markers_pos <- ds2_markers[ds2_markers$avg_logFC>0, ]
ds2_markers_neg <- ds2_markers[ds2_markers$avg_logFC<0, ]
```
```{r}
gene_list <- rownames(ds2_markers_pos)
up_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(up_enrich.go, showCategory = 10)
cnetplot(up_enrich.go, showCategory = 8, colorEdge = T)
emapplot(up_enrich.go, showCategory = 10)

##down-regulated genes
gene_list <- rownames(ds2_markers_neg)
down_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)
ggsave("down_enrich1.svg",device = svg, plot = dotplot(down_enrich.go, showCategory = 10), width = 10, height = 6)

ggsave("down_enrich.svg",device = svg, plot = cnetplot(down_enrich.go,  showCategory = 8, colorEdge = T), width = 10, height = 6)
emapplot(down_enrich.go, showCategory = 10)

```


ds1
```{r}
umapplot(ds1)
ds1_markers <- FindMarkers(ds1,ident.1 = "SMC2",min.diff.pct = 0.2,logfc.threshold = 0.5)
ds1_markers_pos <- ds1_markers[ds1_markers$avg_logFC>0, ]
ds1_markers_neg <- ds1_markers[ds1_markers$avg_logFC<0, ]
```
```{r}
gene_list <- rownames(ds1_markers_pos)
up_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(up_enrich.go, showCategory = 10)
cnetplot(up_enrich.go, showCategory = 10)
emapplot(up_enrich.go, showCategory = 10)

##down-regulated genes
gene_list <- rownames(ds1_markers_neg)
down_enrich.go <- enrichGO(
    gene = gene_list, # 基因列表文件中的基因名称
    OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
    ont = "ALL", # 可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
    pAdjustMethod = "fdr", pvalueCutoff = 0.05, qvalueCutoff = 0.2)

dotplot(down_enrich.go, showCategory = 10)
cnetplot(down_enrich.go, showCategory = 10)
emapplot(down_enrich.go, showCategory = 10)

```
# BMP通路相关
```{r}
Dotplot(c("BMP","BMPR1A","BMPR1B"),ds2)
Dotplot(c("BMPR1A","BMPR1B"),ds1)
f("DLX5",ds2)
```
# 雷达图
```{r}
library(fmsb)

data=as.data.frame(matrix(sample(0:50, 18,replace=T) , ncol=6))
colnames(data)=c('IL-1 signaling pathway','Response to IFNa','NFkB signaling pathway','IL-6 signaling pathway', 'Muscle contraction','Response to IFNr')
rownames(data) <- c('Monocyte','Neutrophil','Macrophage')
# 用于生成雷达图的最大最小值
data=rbind(rep(50,5) , rep(0,5) , data)

colors_list[1:3]
colors_border <- colors_list[1:3]
colors_in <- aero_colors_list[1:3]
radarchart( data , axistype=0,
 pcol=colors_border , pfcol=colors_in , plwd=1.3 , plty=1,pty=32,
 cglcol="black", cglty=3, cglwd=0.6,
)
legend(x=1.5, y=1, legend = rownames(data[-c(1,2),]), bty = "n", pch=20 , col=colors_border, text.col = "black", cex=1, pt.cex=2)
```


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